Psychological Factors Related to Treatment Outcomes in Head and Neck Cancer.

BACKGROUND: Patients with head and neck cancer (HNC) often demonstrate stress, distress, anxiety, depression, and are at risk for suicide. These affect their quality of life (QoL) but less attention has been given to psychological variables that may impact response to treatment. OBJECTIVES: This study aims to systematically review publications during 2013-2023 to collate evidence on the effects of psychological variables on HNC treatment outcomes. METHODS: We searched Ovid Medline, PubMed, Scopus, and Web of Science for articles that examined psychological factors related to treatment outcomes in patients with HNC. RESULTS: There were 29 studies (5 before treatment, 2 during, 17 after, and 5 covering the whole management trajectory) including 362,766 patients. The psychological factors were either behavioral (adjustment and coping strategy, unrealistic ideas, self-blame), cognitive (elevated risk of psychiatric co-comorbidity), or emotional (distress, depression, anxiety, nervousness, and fear of disfigurement and complications). It was found that there was a relationship between depression and decreased survival in patients with HNC. Pretreatment pain was an independent predictor of decreased survival in a large sample of patients. The distress level was approximately  54%, emotional problems ranged between 10 and 44%, while financial difficulties were identified in 54% of the patients. Sixty-nine percent of patients were reported to have used at least one cost-coping strategy within 6 months after treatment initiation. During post-treatment period, depression increased from 15% at the baseline to 29%, while the fear of recurrence was found among at least 35% of patients. DISCUSSION AND CONCLUSION: Several psychological factors predict QoL and survival among HNC survivors. Distress encompasses depression and anxiety, and physical burden from HNC diagnosis and treatment. Routine screening and early interventions that target distress could improve HNC survivors' QoL. A systematic and standardized measurement approach for QoL is warranted to homogenize these findings and to understand the underlying relationships.

Web-based prognostic tools for oral tongue cancer: An analysis of online predictors.

BACKGROUND: Oral tongue squamous cell carcinoma (OTSCC) often presents with aggressive clinical behaviour that may require multimodality treatment based on reliable prognostication. We aimed to evaluate the prognostic ability of five online web-based tools to predict the clinical behaviour of OTSCC resection and biopsy samples. METHODS: A total of 135 OTSCC resection cases and 33 OTSCC biopsies were included to predict recurrence and survival. Area under the receiver operating characteristic curves (AUC), χ2 tests, and calibration plots constructed to estimate the prognostic power of each tool. RESULTS: The tool entitled 'Prediction of risk of Locoregional Recurrences in Early OTSCC' presented an accuracy of 82%. The tool, 'Head & Neck Cancer Outcome Calculator' for 10-year cancer-related mortality had an accuracy 77% and AUC 0.858. The other tool entitled 'Cancer Survival Rates' for 5-year mortality showed an accuracy of 74% and AUC of 0.723. For biopsy samples, 'Cancer Survival Prediction Calculators' predicted the recurrence free survival with an accuracy of 70%. CONCLUSIONS: Web-based tools can aid in clinical decision making of OTSCC. Three of five online web-based tools could predict recurrence risk and cancer-related mortality in resected OTSCC and one tool could help in clinical decision making for biopsy samples.

Artificial Intelligence-Driven Radiomics in Head and Neck Cancer: Current Status and Future Prospects.

BACKGROUND: Radiomics is a rapidly growing field used to leverage medical radiological images by extracting quantitative features. These are supposed to characterize a patient's phenotype, and when combined with artificial intelligence techniques, to improve the accuracy of diagnostic models and clinical outcome prediction. OBJECTIVES: This review aims at examining the application areas of artificial intelligence-based radiomics (AI-based radiomics) for the management of head and neck cancer (HNC). It further explores the workflow of AI-based radiomics for personalized and precision oncology in HNC. Finally, it examines the current challenges of AI-based radiomics in daily clinical oncology and offers possible solutions to these challenges. METHODS: Comprehensive electronic databases (PubMed, Medline via Ovid, Scopus, Web of Science, CINAHL, and Cochrane Library) were searched following the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines. The quality of included studies and their risk of biases were evaluated using the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD)and Prediction Model Risk of Bias Assessment Tool (PROBAST). RESULTS: Out of the 659 search hits retrieved, 45 fulfilled the inclusion criteria. Our review revealed that the application of AI-based radiomics model as an ancillary tool for improved decision-making in HNC management includes radiomics-based cancer diagnosis and radiomics-based cancer prognosis. The radiomics-based cancer diagnosis includes tumor staging, tumor grading, and classification of malignant and benign tumors. Similarly, radiomics-based cancer prognosis includes prediction for treatment response, recurrence, metastasis, and survival. In addition, the challenges in the implementation of these models for clinical evaluations include data imbalance, feature engineering (extraction and selection), model generalizability, multi-modal fusion, and model interpretability. CONCLUSION: Considering the highly subjective and interobserver variability that is peculiar to the interpretation of medical images by expert clinicians, AI-based radiomics seeks to offer potentially useful quantitative information, which is not visible to the human eye or unintentionally often remain ignored during clinical imaging practice. By enabling the extraction of this type of information, AI-based radiomics has the potential to revolutionize HNC oncology, providing a platform for more personalized, higher quality, and cost-effective care for HNC patients.

The prognostic role of single cell invasion and nuclear diameter in early oral tongue squamous cell carcinoma.

BACKGROUND: The clinical significance of single cell invasion and large nuclear diameter is not well documented in early-stage oral tongue squamous cell carcinoma (OTSCC). METHODS: We used hematoxylin and eosin-stained sections to evaluate the presence of single cell invasion and large nuclei in a multicenter cohort of 311 cases treated for early-stage OTSCC. RESULTS: Single cell invasion was associated in multivariable analysis with poor disease-specific survival (DSS) with a hazard ratio (HR) of 2.089 (95% CI 1.224-3.566, P = 0.007), as well as with disease-free survival (DFS) with a HR of 1.666 (95% CI 1.080-2.571, P = 0.021). Furthermore, large nuclei were associated with worse DSS (HR 2.070, 95% CI 1.216-3.523, P = 0.007) and with DFS in multivariable analysis (HR 1.645, 95% CI 1.067-2.538, P = 0.024). CONCLUSION: Single cell invasion and large nuclei can be utilized for classifying early OTSCC into risk groups.

Interpretable machine learning model for prediction of overall survival in laryngeal cancer.

Background: The mortality rates of laryngeal squamous cell carcinoma cancer (LSCC) have not significantly decreased in the last decades.Objectives: We primarily aimed to compare the predictive performance of DeepTables with the state-of-the-art machine learning (ML) algorithms (Voting ensemble, Stack ensemble, and XGBoost) to stratify patients with LSCC into chance of overall survival (OS). In addition, we complemented the developed model by providing interpretability using both global and local model-agnostic techniques.Methods: A total of 2792 patients in the Surveillance, Epidemiology, and End Results (SEER) database diagnosed with LSCC were reviewed. The global model-agnostic interpretability was examined using SHapley Additive exPlanations (SHAP) technique. Likewise, individual interpretation of the prediction was made using Local Interpretable Model Agnostic Explanations (LIME).Results: The state-of-the-art ML ensemble algorithms outperformed DeepTables. Specifically, the examined ensemble algorithms showed comparable weighted area under receiving curve of 76.9, 76.8, and 76.1 with an accuracy of 71.2%, 70.2%, and 71.8%, respectively. The global methods of interpretability (SHAP) demonstrated that the age of the patient at diagnosis, N-stage, T-stage, tumor grade, and marital status are among the prominent parameters.Conclusions: A ML model for OS prediction may serve as an ancillary tool for treatment planning of LSCC patients.